Cooperate Company Selected by Fuzzy Neural Network on Rough Set Theory

2008 ◽  
Author(s):  
Cao Jie ◽  
Chen Sen Fa
Keyword(s):  
Neural Network ◽  
Set Theory ◽  
Rough Set ◽  
Fuzzy Neural Network ◽  
Rough Set Theory ◽  
Fuzzy Neural

Integrating rough set theory and fuzzy neural network to discover fuzzy rules

2003 ◽  
Vol 7 (1) ◽  
pp. 59-73 ◽  
Author(s):  
Shi-tong Wang ◽  
Dong-jun Yu ◽  
Jing-yu Yang
Keyword(s):  
Neural Network ◽  
Set Theory ◽  
Rough Set ◽  
Fuzzy Neural Network ◽  
Rough Set Theory ◽  
Fuzzy Rules ◽  
Fuzzy Neural

Rough Set Fuzzy Neural Network Fault Diagnosis for the over Current Detection of Coal Mining Scraper

2013 ◽  
Vol 321-324 ◽  
pp. 2146-2151
Author(s):  
Yong Qiang Zhang ◽  
Xian Min Ma ◽  
Jian Xiang Yang ◽  
Yan Ni Zhang
Keyword(s):  
Neural Network ◽  
Set Theory ◽  
Rough Set ◽  
Fuzzy Neural Network ◽  
Rough Set Theory ◽  
Driving System ◽  
The Neural Network ◽  
Scraper Conveyor ◽  
Fuzzy Neural ◽  
Network Fault

In this paper the rough set fuzzy neural network is used to monitor the over current fault problem of the mining scraper conveyor motor driving system. The phase current signals are input into the neural network, and then the current signals are processed with fuzzy logic set theory for optimization. Because too many rules may lead to complex computation, the rough set theory is used to reduce the rules after the signal characteristics are extracted. The simulation results show that the precision and reliability of motor driving system of the mining scraper conveyor can be improved by this method.

Research on Performance Evaluation of Finance Transportation Projects Based on Fuzzy Neural Network

2014 ◽  
Vol 667 ◽  
pp. 60-63
Author(s):  
Wei Guo ◽  
Zhen Ji Zhang
Keyword(s):  
Neural Network ◽  
Performance Evaluation ◽  
Set Theory ◽  
Evaluation System ◽  
Fuzzy Neural Network ◽  
Expert Knowledge ◽  
Highway Projects ◽  
Performance Evaluation System ◽  
Transportation Projects ◽  
Fuzzy Neural

A performance evaluation system of finance transportation projects is mainly researched, in which the sub-module of the highway projects evaluation, waterway projects evaluation, Passenger stations projects evaluation, Energy saving projects evaluation are incorporated. In addition, the expert knowledge are inserted in the system, the multi-layer neural network and fuzzy-set theory are used to implement Performance Evaluation system of Finance invest Transportation Projects, and the feasibility and effectiveness of the evaluation system are finally verified by practice.

Design of Neural Network Based on Rough Set

2012 ◽  
Vol 263-266 ◽  
pp. 3378-3381
Author(s):  
Xue Min Zhang ◽  
Zhen Dong Mu
Keyword(s):  
Neural Network ◽  
Set Theory ◽  
Rough Set ◽  
Rough Set Theory ◽  
Neural Network Classification ◽  
The Neural Network ◽  
Sample Test ◽  
Low Efficiency ◽  
The Right ◽  
Test Module

After years of development, the neural network classification, clustering and forecasting applications have a lot of development, but the neural network has the inevitable defects, if you enter the attribute set, the classification boundaries are not clear, convergence low efficiency and accuracy, there may even be the state does not converge, using rough set theory, the right value to modify the function to be modified, and joined the contradictions sample test module, after the use of EEG to verify reached the deletion of number of features and the purpose to improve the classification accuracy.

scholarly journals A rough set theory and deep learning based predictive system for gender recognition using audio speech

2021 ◽  
Author(s):  
Ghazaala Yasmin ◽  
ASIT KUMAR DAS ◽  
Janmenjoy Nayak ◽  
S Vimal ◽  
Soumi Dutta
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Deep Learning ◽  
Set Theory ◽  
Rough Set ◽  
Gender Discrimination ◽  
Rough Set Theory ◽  
Recognition System ◽  
Gender Recognition ◽  
Learning Techniques

Abstract Speech is one of the most delicate medium through which gender of the speakers can easily be identified. Though the related research has shown very good progress in machine learning but recently, deep learning has imparted a very good research area to explore the deficiency of gender discrimination using traditional machine learning techniques. In deep learning techniques, the speech features are automatically generated by the reinforcement learning from the raw data which have more discriminating power than the human generated features. But in some practical situations like gender recognition, it is observed that combination of both types of features sometimes provides comparatively better performance. In the proposed work, we have initially extracted and selected some informative and precise acoustic features relevant to gender recognition using entropy based information theory and Rough Set Theory (RST). Next, the audio speech signals are directly fed into the deep neural network model consists of Convolution Neural Network (CNN) and Gated Recurrent Unit network (GRUN) for extracting features useful for gender recognition. The RST selects precise and informative features, CNN extracts the locally encoded important features, and GRUN reduces the vanishing gradient and exploding gradient problems. Finally, a hybrid gender recognition system is developed combining both generated feature vectors. The developed model has been tested with five bench mark and a simulated dataset to evaluate its performance and it is observed that combined feature vector provides more effective gender recognition system specially when transgender is considered as a gender type together with male and female.

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